Quantifying the impact of Russia–Ukraine crisis on food security and trade pattern: evidence from a structural general equilibrium trade model
Bibliographic record
Abstract
Purpose Considering the importance of Russia and Ukraine in agriculture, the authors quantify the potential impact of the Russia–Ukraine conflict on food output, trade, prices and food security for the world. Design/methodology/approach The authors mainly use the quantitative and structural multi-country and multi-sector general equilibrium trade model to analyze the potential impacts of the conflict on the global food trade pattern and security. Findings First, the authors found that the conflict would lead to soaring agricultural prices, decreasing trade volume and severe food insecurity especially for countries that rely heavily on grain imports from Ukraine and Russia, such as Egypt and Turkey. Second, major production countries such as the United States and Canada may even benefit from the conflict. Third, restrictions on upstream energy and fertilizer will amplify the negative effects of food insecurity. Originality/value This study analyzed the effect of Russia–Ukraine conflict on global food security based on sector linkages and the quantitative general equilibrium trade framework. With a clearer demonstration of the influence about the inherent mechanism based on fewer parameters compared with traditional Global Trade Analysis Project (GTAP) models, the authors showed integrated impacts of the conflict on food output, trade, prices and welfare across sectors and countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".